This section describes how you interact through the Trifacta® platform with your AWS Glue data warehouse via AWS Glue Catalog.
Uses of Glue
The Trifacta platform can use Glue for the following tasks:
- Create datasets by reading from Glue tables.
Before You Begin Using Glue
Read Access: Your Glue administrator must configure read permissions to Glue databases.
Write Access: Not supported.
For more information, see Configure for AWS.
Reading Partitioned Data
The Trifacta platform can read in partitioned tables. However, it cannot read individual partitions of partitioned tables.
Tip: If you are reading data from a partitioned table, one of your early recipe steps in the Transformer page should filter out the unneeded table data so that you are reading only the records of the individual partition.
Storing Data in Glue
Users should know where shared data is located and where personal data can be saved without interfering with or confusing other users.
NOTE: The Trifacta platform does not modify source data in Glue. Datasets sourced from Glue are read without modification from their source locations.
Reading from Glue
You can create a Trifacta dataset from a table or view stored in Glue. For more information, see AWS Glue Browser.
Notes on reading from views using custom SQL
If you have enabled custom SQL and are reading data from a view, nested functions are written to a temporary filename, unless they are explicitly aliased.
Tip: If your custom SQL uses nested functions, you should create an explicit alias from the results. Otherwise, the job is likely to fail.
When these are read from a Glue view, the temporary column names are:
_c1, etc. During job execution, Spark ignores the
In this improved example, the two Glue view columns are aliased to the explicit column names, which are correctly interpreted and used by the Spark running environment during job execution.
Writing to Glue
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